David Rotermund

Optimal short-term population coding: when fisher information fails

Efficient coding has been proposed as a first principle explaining neuronal response properties in the central nervous system. The shape of optimal codes, however, strongly depends on the natural limitations of the particular physical system. Here we investigate how optimal neuronal encoding strategies are influenced by the finite number of neurons N (place constraint), the limited decoding time window length T (time constraint), the maximum neuronal firing rate fmax (power constraint), and the maximal average rate fmax (energy constraint). While Fisher information provides a general lower bound for the mean squared error of unbiased signal reconstruction, its use to characterize the coding precision is limited. Analyzing simple examples, we illustrate some typical pitfalls and thereby show that Fisher information provides a valid measure for the precision of a code only if the dynamic range (fmin T, fmax T) is sufficiently large. In particular, we demonstrate that the optimal width of gaussian tuning curves depends on the available decoding time T. Within the broader class of unimodal tuning functions, it turns out that the shape of a Fisher-optimal coding scheme is not unique. We solve this ambiguity by taking the minimum mean square error into account, which leads to flat tuning curves. The tuning width, however, remains to be determined by energy constraints rather than by the principle of efficient coding.

Attention Improves Object Representation in Visual Cortical Field Potentials

Selective attention improves perception and modulates neuronal responses, but how attention-dependent changes of cortical activity improve the processing of attended objects is an open question. Changes in total signal strength or enhancements in signal-to-noise ratio have been proposed as putative mechanisms. However, it is still not clear whether, and to what extent, these processes contribute to the large perceptual improvements. We studied the ability to discriminate states of activity in visual cortex evoked by differently shaped objects depending on selective attention in monkeys. We found that gamma-band activity from V4 and V1 contains a high amount of information about stimulus shape, which increases for V4 recordings considerably with attention in successful trials, but not in case of behavioral errors. This effect resulted from enhanced differences between the stimulus-specific distributions of power spectral amplitudes. It could be explained neither by enhancements of signal-to-noise ratios, nor by changes in total signal power. Instead our results indicate that attention causes underlying cortical network states to become more distinct for different stimuli, providing a new neurophysiological explanation for improvements of behavioral performance by attention. The absence of the enhancement in discriminability in trials with behavioral errors demonstrates the relevance of this novel neural mechanism for perception.

Optimal neural rate coding leads to bimodal firing rate distributions

Many experimental studies concerning the neuronal code are based on graded responses of neurons, given by the emitted number of spikes measured in a certain time window. Correspondingly, a large body of neural network theory deals with analogue neuron models and discusses their potential use for computation or function approximation. All physical signals, however, are of limited precision, and neuronal firing rates in cortex are relatively low. Here, we investigate the relevance of analogue signal processing with spikes in terms of optimal stimulus reconstruction and information theory. In particular, we derive optimal tuning functions taking the biological constraint of limited firing rates into account. It turns out that depending on the available decoding time T, optimal encoding undergoes a phase transition from discrete binary coding for small T towards analogue or quasi-analogue encoding for large T. The corresponding firing rate distributions are bimodal for all relevant T, in particular in the case of population coding.

Marginally subcritical dynamics explain enhanced stimulus discriminability under attention.

Recent experimental and theoretical work has established the hypothesis that cortical neurons operate close to a critical state which describes a phase transition from chaotic to ordered dynamics. Critical dynamics are suggested to optimize several aspects of neuronal information processing. However, although critical dynamics have been demonstrated in recordings of spontaneously active cortical neurons, little is known about how these dynamics are affected by task-dependent changes in neuronal activity when the cortex is engaged in stimulus processing. Here we explore this question in the context of cortical information processing modulated by selective visual attention. In particular, we focus on recent findings that local field potentials (LFPs) in macaque area V4 demonstrate an increase in γ-band synchrony and a simultaneous enhancement of object representation with attention. We reproduce these results using a model of integrate-and-fire neurons where attention increases synchrony by enhancing the efficacy of recurrent interactions. In the phase space spanned by excitatory and inhibitory coupling strengths, we identify critical points and regions of enhanced discriminability. Furthermore, we quantify encoding capacity using information entropy. We find a rapid enhancement of stimulus discriminability with the emergence of synchrony in the network. Strikingly, only a narrow region in the phase space, at the transition from subcritical to supercritical dynamics, supports the experimentally observed discriminability increase. At the supercritical border of this transition region, information entropy decreases drastically as synchrony sets in. At the subcritical border, entropy is maximized under the assumption of a coarse observation scale. Our results suggest that cortical networks operate at such near-critical states, allowing minimal attentional modulations of network excitability to substantially augment stimulus representation in the LFPs.

Development of a fully implantable recording system for ECoG signals

This paper presents a fully implantable neural recording system for the simultaneous recording of 128 channels. The electrocorticography (ECoG) signals are sensed with 128 gold electrodes embedded in a 10 µm thick polyimide foil. The signals are picked up by eight amplifier array ICs and digitized with a resolution of 16 bit at 10 kHz. The digitized measurement data is processed in a reconfigurable digital ASIC, which is fabricated in a 0.35 µm CMOS technology and occupies an area of 2.8×2.8mm2. After data reduction, the measurement data is fed into a transceiver IC, which transmits the data with up to 495 kbit/s to a base station, using an RF loop antenna on a flexible PCB. The power consumption of 84mW is delivered via inductive coupling from the base station.

Towards On-line Adaptation of Neuro-prostheses with Neuronal Evaluation Signals

Many experiments have successfully demonstrated that prosthetic devices for restoring lost body functions can in principle be controlled by brain signals. However, stable long-term application of these devices, required for paralyzed patients, may suffer substantially from on-going signal changes for example adapting neural activities or movements of the electrodes recording brain activity. These changes currently require tedious re-learning procedures which are conducted and supervised under laboratory conditions, hampering the everyday use of such devices. As an efficient alternative to current methods we here propose an on-line adaptation scheme that exploits a hypothetical secondary signal source from brain regions reflecting the user’s affective evaluation of the current neuro- prosthetic’s performance. For demonstrating the feasibility of our idea, we simulate a typical prosthetic setup controlling a virtual robotic arm. Hereby we use the additional, hypothetical evaluation signal to adapt the decoding of the intended arm movement which is subjected to large non-stationarities. Even with weak signals and high noise levels typically encountered in recording brain activities, our simulations show that prosthetic devices can be adapted successfully during everyday usage, requiring no special training procedures. Furthermore, the adaptation is shown to be stable against large changes in neural encoding and/or in the recording itself.

Toward High Performance, Weakly Invasive Brain Computer Interfaces Using Selective Visual Attention

Brain–computer interfaces have been proposed as a solution for paralyzed persons to communicate and interact with their environment. However, the neural signals used for controlling such prostheses are often noisy and unreliable, resulting in a low performance of real-world applications. Here we propose neural signatures of selective visual attention in epidural recordings as a fast, reliable, and high-performance control signal for brain prostheses. We recorded epidural field potentials with chronically implanted electrode arrays from two macaque monkeys engaged in a shape-tracking task. For single trials, we classified the direction of attention to one of two visual stimuli based on spectral amplitude, coherence, and phase difference in time windows fixed relative to stimulus onset. Classification performances reached up to 99.9%, and the information about attentional states could be transferred at rates exceeding 580 bits/min. Good classification can already be achieved in time windows as short as 200 ms. The classification performance changed dynamically over the trial and modulated with the tasks varying demands for attention. For all three signal features, the information about the direction of attention was contained in the γ-band. The most informative feature was spectral amplitude. Together, these findings establish a novel paradigm for constructing brain prostheses as, for example, virtual spelling boards, promising a major gain in performance and robustness for human brain–computer interfaces.

Efficient Computation Based on Stochastic Spikes

The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neurons internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.

High-performance classification of contour percepts from EEG recordings

Contour integration is a fundamental process for visual scene segmentation and object recognition. Consequently, human observers are very efficient in detecting configurations of aligned edge elements in a background of randomly oriented distracters. Neural signatures of contour integration processes have been found in electrophysiological recordings in the early visual areas of primates, and in EEG signals from the occipital areas in human subjects. However, the corresponding differences in the signals between stimuli containing contours or no contours are normally small, and only show up after extensive averaging over trials. In this contribution, we investigate neural signatures of contour integration processes in EEG recordings by classifying the presence or absence of contours on a trial-by-trial basis from the recorded data. Stimuli consisted of fields of oriented Gabor elements, which were positioned randomly on the screen. Half of the stimuli contained an elliptic contour, which was formed by 13 colinearily aligned edges. In a two-alternative-forced-choice task, 20 observers had to indicate the presence or absence of a contour by pressing a corresponding response button. To our surprise, classification performance on the EEG data can be as high as 78% in single observers, averaging at about 64% over our 20 observers. Given that all stimuli have the same number of ~350 Gabor elements and only differ in the alignment of a small subset of 13 edges, differences in the EEG and in its classification reflect differences between perceptual states, rather than differences between physical stimuli. In the context of constructing EEG-based brain-computer interfaces, these perceptual differences may serve as an additional channel of information for paradigms like SSVEPs which otherwise use different visual stimuli to evoke maximally distinct brain activity patterns. Figure ​Figure11. Figure 1 The left panel shows the difference of the evoked visual potentials (EVPs) for contour stimuli versus non-contour stimuli, averaged over the observers. First signatures of contour integration processes appear at around 180 ms after stimulus onset in the ...

Open Hardware For Neuro-Prosthesis Research: A Study About A Closed-Loop Multi-Channel System For Electrical Surface Stimulations And Measurements

Recent progress in neuro-prosthetic technology gives rise to the hope that in the future blind people might regain some degree of visual perception. It was shown that electrically stimulating the brain can be used to produce simple visual impressions of light blobs (phosphenes). However, this perception is very far away from natural sight. For developing the next generation of visual prostheses, real-time closed-loop stimulators which measure the actual neuronal activities and on this basis determine the required stimulation pattern. This leads to the challenge to design a system that can produce arbitrary stimulation-patterns with up to ±70V and with up to 25mA while measuring neuronal signals with amplitudes in the order of mV. Furthermore, the interruption of the measurement by stimulation must be as short as possible and the system needs to scale to hundreds of electrodes. We discuss how such a system and especially its current pumps and input protection need to be designed and which problems arise. We condense our findings into an example design for which we provide all design files (boards, firmwares and software) as open-source. This is a first step in taking the existing open-source www.open-ephys.org recording system and converting it into a closed-loop experimental setup for neuro-prosthetic research.